Overview

Dataset statistics

Number of variables37
Number of observations5767
Missing cells22370
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory296.0 B

Variable types

Numeric25
Categorical12

Alerts

consol has constant value ""Constant
popsrc has constant value ""Constant
datafmt has constant value ""Constant
curcd has constant value ""Constant
costat has constant value ""Constant
datadate has a high cardinality: 121 distinct valuesHigh cardinality
tic has a high cardinality: 498 distinct valuesHigh cardinality
cusip has a high cardinality: 498 distinct valuesHigh cardinality
conm has a high cardinality: 498 distinct valuesHigh cardinality
ipodate has a high cardinality: 204 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with gvkey and 1 other fieldsHigh correlation
gvkey is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
act is highly overall correlated with at and 14 other fieldsHigh correlation
at is highly overall correlated with act and 13 other fieldsHigh correlation
bkvlps is highly overall correlated with ceql and 3 other fieldsHigh correlation
capx is highly overall correlated with act and 13 other fieldsHigh correlation
ceql is highly overall correlated with act and 14 other fieldsHigh correlation
csho is highly overall correlated with act and 14 other fieldsHigh correlation
dltt is highly overall correlated with act and 12 other fieldsHigh correlation
dt is highly overall correlated with act and 14 other fieldsHigh correlation
emp is highly overall correlated with act and 10 other fieldsHigh correlation
lct is highly overall correlated with act and 13 other fieldsHigh correlation
lt is highly overall correlated with act and 13 other fieldsHigh correlation
ni is highly overall correlated with act and 13 other fieldsHigh correlation
ppegt is highly overall correlated with act and 13 other fieldsHigh correlation
pstkl is highly overall correlated with indfmtHigh correlation
seq is highly overall correlated with act and 13 other fieldsHigh correlation
xad is highly overall correlated with act and 14 other fieldsHigh correlation
xrd is highly overall correlated with act and 1 other fieldsHigh correlation
cik is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
mkvalt is highly overall correlated with act and 14 other fieldsHigh correlation
gsector is highly overall correlated with indfmtHigh correlation
indfmt is highly overall correlated with bkvlps and 9 other fieldsHigh correlation
size_category is highly overall correlated with bkvlps and 1 other fieldsHigh correlation
act has 1614 (28.0%) missing valuesMissing
bkvlps has 955 (16.6%) missing valuesMissing
capx has 918 (15.9%) missing valuesMissing
ceql has 909 (15.8%) missing valuesMissing
csho has 954 (16.5%) missing valuesMissing
dt has 1433 (24.8%) missing valuesMissing
emp has 80 (1.4%) missing valuesMissing
lct has 1611 (27.9%) missing valuesMissing
ni has 910 (15.8%) missing valuesMissing
ppegt has 756 (13.1%) missing valuesMissing
pstkl has 917 (15.9%) missing valuesMissing
xad has 3605 (62.5%) missing valuesMissing
xrd has 2938 (50.9%) missing valuesMissing
mkvalt has 689 (11.9%) missing valuesMissing
ipodate has 3232 (56.0%) missing valuesMissing
size_category has 689 (11.9%) missing valuesMissing
bkvlps is highly skewed (γ1 = 47.36237195)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
capx has 309 (5.4%) zerosZeros
dltt has 283 (4.9%) zerosZeros
dt has 237 (4.1%) zerosZeros
ppegt has 223 (3.9%) zerosZeros
pstkl has 4147 (71.9%) zerosZeros
xrd has 651 (11.3%) zerosZeros

Reproduction

Analysis started2023-05-08 04:18:01.614352
Analysis finished2023-05-08 04:19:03.259307
Duration1 minute and 1.64 second
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct5767
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2883
Minimum0
Maximum5766
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:03.353381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile288.3
Q11441.5
median2883
Q34324.5
95-th percentile5477.7
Maximum5766
Range5766
Interquartile range (IQR)2883

Descriptive statistics

Standard deviation1664.9338
Coefficient of variation (CV)0.57750046
Kurtosis-1.2
Mean2883
Median Absolute Deviation (MAD)1442
Skewness0
Sum16626261
Variance2772004.7
MonotonicityStrictly increasing
2023-05-08T00:19:03.457229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
3852 1
 
< 0.1%
3850 1
 
< 0.1%
3849 1
 
< 0.1%
3848 1
 
< 0.1%
3847 1
 
< 0.1%
3846 1
 
< 0.1%
3845 1
 
< 0.1%
3844 1
 
< 0.1%
3843 1
 
< 0.1%
Other values (5757) 5757
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
5766 1
< 0.1%
5765 1
< 0.1%
5764 1
< 0.1%
5763 1
< 0.1%
5762 1
< 0.1%
5761 1
< 0.1%
5760 1
< 0.1%
5759 1
< 0.1%
5758 1
< 0.1%
5757 1
< 0.1%

gvkey
Real number (ℝ)

Distinct498
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47505.344
Minimum1045
Maximum316056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:04.204748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1045
5-th percentile2019
Q16375
median13714
Q364410
95-th percentile177267
Maximum316056
Range315011
Interquartile range (IQR)58035

Descriptive statistics

Standard deviation62000.425
Coefficient of variation (CV)1.3051253
Kurtosis0.81636136
Mean47505.344
Median Absolute Deviation (MAD)10746
Skewness1.4040962
Sum2.7396332 × 108
Variance3.8440528 × 109
MonotonicityIncreasing
2023-05-08T00:19:04.305898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10903 20
 
0.3%
4640 20
 
0.3%
5786 20
 
0.3%
10035 20
 
0.3%
10096 20
 
0.3%
133768 20
 
0.3%
5543 20
 
0.3%
28034 20
 
0.3%
27914 20
 
0.3%
10614 20
 
0.3%
Other values (488) 5567
96.5%
ValueCountFrequency (%)
1045 10
0.2%
1075 10
0.2%
1078 10
0.2%
1161 10
0.2%
1209 10
0.2%
1230 10
0.2%
1300 10
0.2%
1327 10
0.2%
1380 10
0.2%
1440 10
0.2%
ValueCountFrequency (%)
316056 9
0.2%
294524 10
0.2%
260774 20
0.3%
189491 10
0.2%
187697 10
0.2%
187450 10
0.2%
186989 10
0.2%
186310 10
0.2%
185532 10
0.2%
184996 10
0.2%

datadate
Categorical

Distinct121
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
12/31/19
472 
12/31/18
471 
12/31/17
470 
12/31/16
468 
12/31/15
465 
Other values (116)
3421 

Length

Max length8
Median length8
Mean length7.8226114
Min length7

Characters and Unicode

Total characters45113
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12/31/10
2nd row12/31/11
3rd row12/31/12
4th row12/31/13
5th row12/31/14

Common Values

ValueCountFrequency (%)
12/31/19 472
 
8.2%
12/31/18 471
 
8.2%
12/31/17 470
 
8.1%
12/31/16 468
 
8.1%
12/31/15 465
 
8.1%
12/31/14 460
 
8.0%
12/31/13 456
 
7.9%
12/31/12 445
 
7.7%
12/31/11 439
 
7.6%
12/31/10 436
 
7.6%
Other values (111) 1185
20.5%

Length

2023-05-08T00:19:04.392163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12/31/19 472
 
8.2%
12/31/18 471
 
8.2%
12/31/17 470
 
8.1%
12/31/16 468
 
8.1%
12/31/15 465
 
8.1%
12/31/14 460
 
8.0%
12/31/13 456
 
7.9%
12/31/12 445
 
7.7%
12/31/11 439
 
7.6%
12/31/10 436
 
7.6%
Other values (111) 1185
20.5%

Most occurring characters

ValueCountFrequency (%)
1 16421
36.4%
/ 11534
25.6%
3 6409
 
14.2%
2 5207
 
11.5%
0 1274
 
2.8%
9 870
 
1.9%
6 807
 
1.8%
5 681
 
1.5%
8 663
 
1.5%
7 625
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33579
74.4%
Other Punctuation 11534
 
25.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16421
48.9%
3 6409
 
19.1%
2 5207
 
15.5%
0 1274
 
3.8%
9 870
 
2.6%
6 807
 
2.4%
5 681
 
2.0%
8 663
 
2.0%
7 625
 
1.9%
4 622
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/ 11534
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45113
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16421
36.4%
/ 11534
25.6%
3 6409
 
14.2%
2 5207
 
11.5%
0 1274
 
2.8%
9 870
 
1.9%
6 807
 
1.8%
5 681
 
1.5%
8 663
 
1.5%
7 625
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16421
36.4%
/ 11534
25.6%
3 6409
 
14.2%
2 5207
 
11.5%
0 1274
 
2.8%
9 870
 
1.9%
6 807
 
1.8%
5 681
 
1.5%
8 663
 
1.5%
7 625
 
1.4%

fyear
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2014.4969
Minimum2009
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:04.457328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2010
Q12012
median2015
Q32017
95-th percentile2019
Maximum2019
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8835058
Coefficient of variation (CV)0.0014313777
Kurtosis-1.1987309
Mean2014.4969
Median Absolute Deviation (MAD)2
Skewness-0.029503098
Sum11607531
Variance8.3146058
MonotonicityNot monotonic
2023-05-08T00:19:04.523321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2017 585
10.1%
2018 585
10.1%
2016 584
10.1%
2015 582
10.1%
2014 576
10.0%
2013 572
9.9%
2012 564
9.8%
2019 562
9.7%
2011 557
9.7%
2010 554
9.6%
ValueCountFrequency (%)
2009 41
 
0.7%
2010 554
9.6%
2011 557
9.7%
2012 564
9.8%
2013 572
9.9%
2014 576
10.0%
2015 582
10.1%
2016 584
10.1%
2017 585
10.1%
2018 585
10.1%
ValueCountFrequency (%)
2019 562
9.7%
2018 585
10.1%
2017 585
10.1%
2016 584
10.1%
2015 582
10.1%
2014 576
10.0%
2013 572
9.9%
2012 564
9.8%
2011 557
9.7%
2010 554
9.6%

indfmt
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
INDL
4887 
FS
880 

Length

Max length4
Median length4
Mean length3.6948153
Min length2

Characters and Unicode

Total characters21308
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDL
2nd rowINDL
3rd rowINDL
4th rowINDL
5th rowINDL

Common Values

ValueCountFrequency (%)
INDL 4887
84.7%
FS 880
 
15.3%

Length

2023-05-08T00:19:04.598799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:19:04.676949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
indl 4887
84.7%
fs 880
 
15.3%

Most occurring characters

ValueCountFrequency (%)
I 4887
22.9%
N 4887
22.9%
D 4887
22.9%
L 4887
22.9%
F 880
 
4.1%
S 880
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21308
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 4887
22.9%
N 4887
22.9%
D 4887
22.9%
L 4887
22.9%
F 880
 
4.1%
S 880
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 21308
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 4887
22.9%
N 4887
22.9%
D 4887
22.9%
L 4887
22.9%
F 880
 
4.1%
S 880
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 4887
22.9%
N 4887
22.9%
D 4887
22.9%
L 4887
22.9%
F 880
 
4.1%
S 880
 
4.1%

consol
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
C
5767 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5767
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 5767
100.0%

Length

2023-05-08T00:19:04.738211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:19:04.806313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
c 5767
100.0%

Most occurring characters

ValueCountFrequency (%)
C 5767
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5767
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 5767
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5767
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 5767
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 5767
100.0%

popsrc
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
D
5767 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5767
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 5767
100.0%

Length

2023-05-08T00:19:04.863110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:19:04.930593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
d 5767
100.0%

Most occurring characters

ValueCountFrequency (%)
D 5767
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5767
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 5767
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5767
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 5767
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 5767
100.0%

datafmt
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
STD
5767 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17301
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTD
2nd rowSTD
3rd rowSTD
4th rowSTD
5th rowSTD

Common Values

ValueCountFrequency (%)
STD 5767
100.0%

Length

2023-05-08T00:19:04.986412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:19:05.053260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
std 5767
100.0%

Most occurring characters

ValueCountFrequency (%)
S 5767
33.3%
T 5767
33.3%
D 5767
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17301
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 5767
33.3%
T 5767
33.3%
D 5767
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 17301
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 5767
33.3%
T 5767
33.3%
D 5767
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 5767
33.3%
T 5767
33.3%
D 5767
33.3%

tic
Categorical

Distinct498
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
UNH
 
20
FITB
 
20
HBAN
 
20
STT
 
20
PSA
 
20
Other values (493)
5667 

Length

Max length5
Median length3
Mean length3.1288365
Min length1

Characters and Unicode

Total characters18044
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAAL
2nd rowAAL
3rd rowAAL
4th rowAAL
5th rowAAL

Common Values

ValueCountFrequency (%)
UNH 20
 
0.3%
FITB 20
 
0.3%
HBAN 20
 
0.3%
STT 20
 
0.3%
PSA 20
 
0.3%
MET 20
 
0.3%
WELL 20
 
0.3%
CB 20
 
0.3%
HUM 20
 
0.3%
GL 20
 
0.3%
Other values (488) 5567
96.5%

Length

2023-05-08T00:19:05.118040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unh 20
 
0.3%
key 20
 
0.3%
jpm 20
 
0.3%
brk.b 20
 
0.3%
tfc 20
 
0.3%
peak 20
 
0.3%
cboe 20
 
0.3%
dfs 20
 
0.3%
wfc 20
 
0.3%
exr 20
 
0.3%
Other values (488) 5567
96.5%

Most occurring characters

ValueCountFrequency (%)
A 1311
 
7.3%
C 1308
 
7.2%
T 1125
 
6.2%
M 1062
 
5.9%
S 1062
 
5.9%
R 1060
 
5.9%
L 964
 
5.3%
E 946
 
5.2%
P 920
 
5.1%
N 870
 
4.8%
Other values (17) 7416
41.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18014
99.8%
Other Punctuation 30
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1311
 
7.3%
C 1308
 
7.3%
T 1125
 
6.2%
M 1062
 
5.9%
S 1062
 
5.9%
R 1060
 
5.9%
L 964
 
5.4%
E 946
 
5.3%
P 920
 
5.1%
N 870
 
4.8%
Other values (16) 7386
41.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18014
99.8%
Common 30
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1311
 
7.3%
C 1308
 
7.3%
T 1125
 
6.2%
M 1062
 
5.9%
S 1062
 
5.9%
R 1060
 
5.9%
L 964
 
5.4%
E 946
 
5.3%
P 920
 
5.1%
N 870
 
4.8%
Other values (16) 7386
41.0%
Common
ValueCountFrequency (%)
. 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18044
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1311
 
7.3%
C 1308
 
7.2%
T 1125
 
6.2%
M 1062
 
5.9%
S 1062
 
5.9%
R 1060
 
5.9%
L 964
 
5.3%
E 946
 
5.2%
P 920
 
5.1%
N 870
 
4.8%
Other values (17) 7416
41.1%

cusip
Categorical

Distinct498
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
91324P102
 
20
316773100
 
20
446150104
 
20
857477103
 
20
74460D109
 
20
Other values (493)
5667 

Length

Max length9
Median length9
Mean length8.8976938
Min length7

Characters and Unicode

Total characters51313
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row02376R102
2nd row02376R102
3rd row02376R102
4th row02376R102
5th row02376R102

Common Values

ValueCountFrequency (%)
91324P102 20
 
0.3%
316773100 20
 
0.3%
446150104 20
 
0.3%
857477103 20
 
0.3%
74460D109 20
 
0.3%
59156R108 20
 
0.3%
95040Q104 20
 
0.3%
H1467J104 20
 
0.3%
444859102 20
 
0.3%
3.80E+106 20
 
0.3%
Other values (488) 5567
96.5%

Length

2023-05-08T00:19:05.202257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
91324p102 20
 
0.3%
493267108 20
 
0.3%
46625h100 20
 
0.3%
84670702 20
 
0.3%
89832q109 20
 
0.3%
42250p103 20
 
0.3%
12503m108 20
 
0.3%
254709108 20
 
0.3%
949746101 20
 
0.3%
30225t102 20
 
0.3%
Other values (488) 5567
96.5%

Most occurring characters

ValueCountFrequency (%)
0 9435
18.4%
1 9373
18.3%
4 4297
8.4%
2 3945
7.7%
3 3885
7.6%
5 3863
7.5%
6 3727
 
7.3%
7 3360
 
6.5%
8 3309
 
6.4%
9 3203
 
6.2%
Other values (25) 2916
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48397
94.3%
Uppercase Letter 2694
 
5.3%
Other Punctuation 111
 
0.2%
Math Symbol 111
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 348
 
12.9%
L 191
 
7.1%
R 170
 
6.3%
C 162
 
6.0%
P 160
 
5.9%
E 151
 
5.6%
T 150
 
5.6%
V 145
 
5.4%
H 140
 
5.2%
X 131
 
4.9%
Other values (13) 946
35.1%
Decimal Number
ValueCountFrequency (%)
0 9435
19.5%
1 9373
19.4%
4 4297
8.9%
2 3945
8.2%
3 3885
8.0%
5 3863
8.0%
6 3727
 
7.7%
7 3360
 
6.9%
8 3309
 
6.8%
9 3203
 
6.6%
Other Punctuation
ValueCountFrequency (%)
. 111
100.0%
Math Symbol
ValueCountFrequency (%)
+ 111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48619
94.7%
Latin 2694
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 348
 
12.9%
L 191
 
7.1%
R 170
 
6.3%
C 162
 
6.0%
P 160
 
5.9%
E 151
 
5.6%
T 150
 
5.6%
V 145
 
5.4%
H 140
 
5.2%
X 131
 
4.9%
Other values (13) 946
35.1%
Common
ValueCountFrequency (%)
0 9435
19.4%
1 9373
19.3%
4 4297
8.8%
2 3945
8.1%
3 3885
8.0%
5 3863
7.9%
6 3727
 
7.7%
7 3360
 
6.9%
8 3309
 
6.8%
9 3203
 
6.6%
Other values (2) 222
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9435
18.4%
1 9373
18.3%
4 4297
8.4%
2 3945
7.7%
3 3885
7.6%
5 3863
7.5%
6 3727
 
7.3%
7 3360
 
6.5%
8 3309
 
6.4%
9 3203
 
6.2%
Other values (25) 2916
 
5.7%

conm
Categorical

Distinct498
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
UNITEDHEALTH GROUP INC
 
20
FIFTH THIRD BANCORP
 
20
HUNTINGTON BANCSHARES
 
20
STATE STREET CORP
 
20
PUBLIC STORAGE
 
20
Other values (493)
5667 

Length

Max length28
Median length20
Mean length17.441998
Min length5

Characters and Unicode

Total characters100588
Distinct characters37
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAMERICAN AIRLINES GROUP INC
2nd rowAMERICAN AIRLINES GROUP INC
3rd rowAMERICAN AIRLINES GROUP INC
4th rowAMERICAN AIRLINES GROUP INC
5th rowAMERICAN AIRLINES GROUP INC

Common Values

ValueCountFrequency (%)
UNITEDHEALTH GROUP INC 20
 
0.3%
FIFTH THIRD BANCORP 20
 
0.3%
HUNTINGTON BANCSHARES 20
 
0.3%
STATE STREET CORP 20
 
0.3%
PUBLIC STORAGE 20
 
0.3%
METLIFE INC 20
 
0.3%
WELLTOWER INC 20
 
0.3%
CHUBB LTD 20
 
0.3%
HUMANA INC 20
 
0.3%
GLOBE LIFE INC 20
 
0.3%
Other values (488) 5567
96.5%

Length

2023-05-08T00:19:05.288886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 2903
 
17.7%
corp 1304
 
8.0%
co 502
 
3.1%
group 346
 
2.1%
financial 261
 
1.6%
253
 
1.5%
energy 180
 
1.1%
plc 154
 
0.9%
technologies 125
 
0.8%
holdings 124
 
0.8%
Other values (703) 10230
62.4%

Most occurring characters

ValueCountFrequency (%)
10645
10.6%
C 8794
 
8.7%
N 8772
 
8.7%
I 8253
 
8.2%
E 7793
 
7.7%
R 7347
 
7.3%
O 7284
 
7.2%
A 6431
 
6.4%
T 5010
 
5.0%
S 4863
 
4.8%
Other values (27) 25396
25.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 88901
88.4%
Space Separator 10645
 
10.6%
Other Punctuation 414
 
0.4%
Dash Punctuation 216
 
0.2%
Open Punctuation 181
 
0.2%
Close Punctuation 181
 
0.2%
Decimal Number 50
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 8794
9.9%
N 8772
9.9%
I 8253
9.3%
E 7793
 
8.8%
R 7347
 
8.3%
O 7284
 
8.2%
A 6431
 
7.2%
T 5010
 
5.6%
S 4863
 
5.5%
L 4478
 
5.0%
Other values (16) 19876
22.4%
Other Punctuation
ValueCountFrequency (%)
& 283
68.4%
' 51
 
12.3%
. 50
 
12.1%
/ 30
 
7.2%
Decimal Number
ValueCountFrequency (%)
6 20
40.0%
3 20
40.0%
5 10
20.0%
Space Separator
ValueCountFrequency (%)
10645
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 216
100.0%
Open Punctuation
ValueCountFrequency (%)
( 181
100.0%
Close Punctuation
ValueCountFrequency (%)
) 181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88901
88.4%
Common 11687
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 8794
9.9%
N 8772
9.9%
I 8253
9.3%
E 7793
 
8.8%
R 7347
 
8.3%
O 7284
 
8.2%
A 6431
 
7.2%
T 5010
 
5.6%
S 4863
 
5.5%
L 4478
 
5.0%
Other values (16) 19876
22.4%
Common
ValueCountFrequency (%)
10645
91.1%
& 283
 
2.4%
- 216
 
1.8%
( 181
 
1.5%
) 181
 
1.5%
' 51
 
0.4%
. 50
 
0.4%
/ 30
 
0.3%
6 20
 
0.2%
3 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10645
10.6%
C 8794
 
8.7%
N 8772
 
8.7%
I 8253
 
8.2%
E 7793
 
7.7%
R 7347
 
7.3%
O 7284
 
7.2%
A 6431
 
6.4%
T 5010
 
5.0%
S 4863
 
4.8%
Other values (27) 25396
25.2%

curcd
Categorical

Distinct1
Distinct (%)< 0.1%
Missing5
Missing (%)0.1%
Memory size45.2 KiB
USD
5762 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17286
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 5762
99.9%
(Missing) 5
 
0.1%

Length

2023-05-08T00:19:05.366154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:19:05.435141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 5762
100.0%

Most occurring characters

ValueCountFrequency (%)
U 5762
33.3%
S 5762
33.3%
D 5762
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17286
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 5762
33.3%
S 5762
33.3%
D 5762
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 17286
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 5762
33.3%
S 5762
33.3%
D 5762
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 5762
33.3%
S 5762
33.3%
D 5762
33.3%

fyr
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10.818292
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:05.492183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q112
median12
Q312
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7145244
Coefficient of variation (CV)0.25091987
Kurtosis4.6402059
Mean10.818292
Median Absolute Deviation (MAD)0
Skewness-2.3753362
Sum62335
Variance7.3686429
MonotonicityNot monotonic
2023-05-08T00:19:05.557072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 4577
79.4%
9 278
 
4.8%
6 222
 
3.8%
1 168
 
2.9%
10 115
 
2.0%
5 98
 
1.7%
3 90
 
1.6%
8 60
 
1.0%
11 47
 
0.8%
4 44
 
0.8%
Other values (2) 63
 
1.1%
ValueCountFrequency (%)
1 168
2.9%
2 23
 
0.4%
3 90
 
1.6%
4 44
 
0.8%
5 98
 
1.7%
6 222
3.8%
7 40
 
0.7%
8 60
 
1.0%
9 278
4.8%
10 115
2.0%
ValueCountFrequency (%)
12 4577
79.4%
11 47
 
0.8%
10 115
 
2.0%
9 278
 
4.8%
8 60
 
1.0%
7 40
 
0.7%
6 222
 
3.8%
5 98
 
1.7%
4 44
 
0.8%
3 90
 
1.6%

act
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3935
Distinct (%)94.8%
Missing1614
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean8652.3032
Minimum25.037
Maximum175552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:05.641646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum25.037
5-th percentile501.0752
Q11606
median3432.828
Q38677
95-th percentile36251.52
Maximum175552
Range175526.96
Interquartile range (IQR)7071

Descriptive statistics

Standard deviation14977.409
Coefficient of variation (CV)1.7310314
Kurtosis28.422244
Mean8652.3032
Median Absolute Deviation (MAD)2360.624
Skewness4.4586764
Sum35933015
Variance2.2432277 × 108
MonotonicityNot monotonic
2023-05-08T00:19:05.735800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10997 3
 
0.1%
8734 3
 
0.1%
15823 3
 
0.1%
19023 3
 
0.1%
6370 3
 
0.1%
1527 3
 
0.1%
6856 2
 
< 0.1%
3557 2
 
< 0.1%
7828 2
 
< 0.1%
67979 2
 
< 0.1%
Other values (3925) 4127
71.6%
(Missing) 1614
 
28.0%
ValueCountFrequency (%)
25.037 1
< 0.1%
30.933 1
< 0.1%
44.173 1
< 0.1%
47.492 1
< 0.1%
50.275 1
< 0.1%
52.956 1
< 0.1%
61.968 1
< 0.1%
66.025 1
< 0.1%
69.273 1
< 0.1%
76.785 1
< 0.1%
ValueCountFrequency (%)
175552 1
< 0.1%
169662 1
< 0.1%
162819 1
< 0.1%
159851 1
< 0.1%
152578 1
< 0.1%
139660 1
< 0.1%
135676 1
< 0.1%
131339 1
< 0.1%
128645 1
< 0.1%
124712 1
< 0.1%

at
Real number (ℝ)

Distinct4833
Distinct (%)84.2%
Missing28
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean85450.629
Minimum49.086
Maximum2687379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:05.835883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum49.086
5-th percentile1713.3258
Q16465.778
median18006.995
Q350915.443
95-th percentile322044
Maximum2687379
Range2687329.9
Interquartile range (IQR)44449.665

Descriptive statistics

Standard deviation266791.93
Coefficient of variation (CV)3.1221763
Kurtosis46.306152
Mean85450.629
Median Absolute Deviation (MAD)13820.005
Skewness6.4306149
Sum4.9040116 × 108
Variance7.1177933 × 1010
MonotonicityNot monotonic
2023-05-08T00:19:05.939099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17708 3
 
0.1%
5838 3
 
0.1%
72574 3
 
0.1%
20386 3
 
0.1%
9132 3
 
0.1%
121347 3
 
0.1%
6225 3
 
0.1%
10285.728 2
 
< 0.1%
9663.63 2
 
< 0.1%
35046.1 2
 
< 0.1%
Other values (4823) 5712
99.0%
(Missing) 28
 
0.5%
ValueCountFrequency (%)
49.086 1
< 0.1%
51.369 1
< 0.1%
59.504 1
< 0.1%
74.998 1
< 0.1%
77.164 1
< 0.1%
106 1
< 0.1%
106.159 1
< 0.1%
106.242 1
< 0.1%
108.746 1
< 0.1%
116.669 1
< 0.1%
ValueCountFrequency (%)
2687379 2
< 0.1%
2622532 2
< 0.1%
2573126 2
< 0.1%
2533600 2
< 0.1%
2490972 2
< 0.1%
2434079 2
< 0.1%
2415689 2
< 0.1%
2359141 2
< 0.1%
2354507 2
< 0.1%
2351698 2
< 0.1%

bkvlps
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4795
Distinct (%)99.6%
Missing955
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean7929.4908
Minimum-125.6877
Maximum16297416
Zeros0
Zeros (%)0.0%
Negative182
Negative (%)3.2%
Memory size45.2 KiB
2023-05-08T00:19:06.037829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-125.6877
5-th percentile1.519805
Q19.953625
median18.384
Q332.573475
95-th percentile72.674025
Maximum16297416
Range16297542
Interquartile range (IQR)22.61985

Descriptive statistics

Standard deviation327827.78
Coefficient of variation (CV)41.342855
Kurtosis2288.1667
Mean7929.4908
Median Absolute Deviation (MAD)10.1635
Skewness47.362372
Sum38156710
Variance1.0747106 × 1011
MonotonicityNot monotonic
2023-05-08T00:19:06.136390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.4568 2
 
< 0.1%
5.4405 2
 
< 0.1%
21.4275 2
 
< 0.1%
19.8094 2
 
< 0.1%
32.4421 2
 
< 0.1%
13.8905 2
 
< 0.1%
10.3063 2
 
< 0.1%
7.7707 2
 
< 0.1%
60.1364 2
 
< 0.1%
10.9857 2
 
< 0.1%
Other values (4785) 4792
83.1%
(Missing) 955
 
16.6%
ValueCountFrequency (%)
-125.6877 1
< 0.1%
-87.732 1
< 0.1%
-74.1842 1
< 0.1%
-71.2976 1
< 0.1%
-70.1814 1
< 0.1%
-63.7648 1
< 0.1%
-61.3894 1
< 0.1%
-59.0613 1
< 0.1%
-56.826 1
< 0.1%
-55.494 1
< 0.1%
ValueCountFrequency (%)
16297416 1
< 0.1%
15437843 1
< 0.1%
2455471 1
< 0.1%
1956293 1
< 0.1%
1885901 1
< 0.1%
644.4382 1
< 0.1%
505.4673 1
< 0.1%
434.9748 1
< 0.1%
353.2199 1
< 0.1%
325.8986 1
< 0.1%

capx
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct3888
Distinct (%)80.2%
Missing918
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean1241.3741
Minimum0
Maximum37985
Zeros309
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:06.241923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q183.435
median280.873
Q31113
95-th percentile5164.8
Maximum37985
Range37985
Interquartile range (IQR)1029.565

Descriptive statistics

Standard deviation2924.7561
Coefficient of variation (CV)2.3560635
Kurtosis44.020255
Mean1241.3741
Median Absolute Deviation (MAD)255.191
Skewness5.7506115
Sum6019422.9
Variance8554198.5
MonotonicityNot monotonic
2023-05-08T00:19:06.340767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 309
 
5.4%
119 7
 
0.1%
162 7
 
0.1%
121 7
 
0.1%
145 7
 
0.1%
593 7
 
0.1%
180 7
 
0.1%
246 6
 
0.1%
112 5
 
0.1%
224 5
 
0.1%
Other values (3878) 4482
77.7%
(Missing) 918
 
15.9%
ValueCountFrequency (%)
0 309
5.4%
0.42 1
 
< 0.1%
0.899 1
 
< 0.1%
1.384 1
 
< 0.1%
1.506 1
 
< 0.1%
1.539 1
 
< 0.1%
1.584 1
 
< 0.1%
1.945 1
 
< 0.1%
2.031 1
 
< 0.1%
2.2 1
 
< 0.1%
ValueCountFrequency (%)
37985 1
< 0.1%
35407 1
< 0.1%
34271 1
< 0.1%
33669 1
< 0.1%
32952 1
< 0.1%
30975 1
< 0.1%
30938 1
< 0.1%
29504 1
< 0.1%
29166 1
< 0.1%
27633 1
< 0.1%

ceql
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4785
Distinct (%)98.5%
Missing909
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean13133.283
Minimum-13244
Maximum424791
Zeros0
Zeros (%)0.0%
Negative182
Negative (%)3.2%
Memory size45.2 KiB
2023-05-08T00:19:06.448384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-13244
5-th percentile187.0476
Q11955.957
median4995.85
Q311724.797
95-th percentile51662.85
Maximum424791
Range438035
Interquartile range (IQR)9768.84

Descriptive statistics

Standard deviation28367.252
Coefficient of variation (CV)2.1599514
Kurtosis41.736409
Mean13133.283
Median Absolute Deviation (MAD)3681.3425
Skewness5.6140678
Sum63801487
Variance8.0470099 × 108
MonotonicityNot monotonic
2023-05-08T00:19:06.543494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11829 2
 
< 0.1%
6070 2
 
< 0.1%
4752 2
 
< 0.1%
1145 2
 
< 0.1%
-60 2
 
< 0.1%
-102 2
 
< 0.1%
1454 2
 
< 0.1%
7336 2
 
< 0.1%
8380 2
 
< 0.1%
1045.5 2
 
< 0.1%
Other values (4775) 4838
83.9%
(Missing) 909
 
15.8%
ValueCountFrequency (%)
-13244 1
< 0.1%
-12688 1
< 0.1%
-12629 1
< 0.1%
-12459 1
< 0.1%
-12086 1
< 0.1%
-11926 1
< 0.1%
-11577 1
< 0.1%
-9660 1
< 0.1%
-8617 1
< 0.1%
-8446 1
< 0.1%
ValueCountFrequency (%)
424791 1
< 0.1%
348703 1
< 0.1%
348296 1
< 0.1%
283001 1
< 0.1%
255550 1
< 0.1%
244823 1
< 0.1%
242999 1
< 0.1%
241620 1
< 0.1%
241409 1
< 0.1%
240170 1
< 0.1%

csho
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4641
Distinct (%)96.4%
Missing954
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean597.31446
Minimum0.001
Maximum29058.361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:06.641494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile47.3654
Q1129.954
median281
Q3561
95-th percentile2098.8932
Maximum29058.361
Range29058.36
Interquartile range (IQR)431.046

Descriptive statistics

Standard deviation1161.9103
Coefficient of variation (CV)1.9452238
Kurtosis98.837772
Mean597.31446
Median Absolute Deviation (MAD)174
Skewness7.1858145
Sum2874874.5
Variance1350035.5
MonotonicityNot monotonic
2023-05-08T00:19:06.737188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
325.811 9
 
0.2%
242.6 7
 
0.1%
0.001 5
 
0.1%
333 4
 
0.1%
321 4
 
0.1%
533 3
 
0.1%
280 3
 
0.1%
147 3
 
0.1%
504 3
 
0.1%
967 3
 
0.1%
Other values (4631) 4769
82.7%
(Missing) 954
 
16.5%
ValueCountFrequency (%)
0.001 5
0.1%
0.271 2
 
< 0.1%
0.843 1
 
< 0.1%
1.011 1
 
< 0.1%
1.013 1
 
< 0.1%
1.015 1
 
< 0.1%
1.698 1
 
< 0.1%
2.782 1
 
< 0.1%
2.81 1
 
< 0.1%
3.578 1
 
< 0.1%
ValueCountFrequency (%)
29058.361 1
< 0.1%
10778.264 1
< 0.1%
10615.376 1
< 0.1%
10591.808 1
< 0.1%
10573.017 1
< 0.1%
10535.938 1
< 0.1%
10516.542 1
< 0.1%
10405.625 1
< 0.1%
10380.265 1
< 0.1%
10287.302 1
< 0.1%

dltt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4422
Distinct (%)77.3%
Missing47
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean12190.84
Minimum0
Maximum359180
Zeros283
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:06.834854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.18525
Q11270.5793
median3925.25
Q39773.75
95-th percentile37949
Maximum359180
Range359180
Interquartile range (IQR)8503.1707

Descriptive statistics

Standard deviation31786.695
Coefficient of variation (CV)2.6074245
Kurtosis37.874606
Mean12190.84
Median Absolute Deviation (MAD)3182.1335
Skewness5.7859898
Sum69731605
Variance1.010394 × 109
MonotonicityNot monotonic
2023-05-08T00:19:06.929286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 283
 
4.9%
250 13
 
0.2%
500 8
 
0.1%
150 7
 
0.1%
3669 5
 
0.1%
2106.8 4
 
0.1%
5199 4
 
0.1%
499 4
 
0.1%
45 4
 
0.1%
1516 4
 
0.1%
Other values (4412) 5384
93.4%
(Missing) 47
 
0.8%
ValueCountFrequency (%)
0 283
4.9%
0.018 1
 
< 0.1%
0.022 1
 
< 0.1%
0.038 1
 
< 0.1%
0.193 1
 
< 0.1%
0.349 2
 
< 0.1%
0.457 1
 
< 0.1%
0.485 1
 
< 0.1%
0.509 1
 
< 0.1%
0.648 2
 
< 0.1%
ValueCountFrequency (%)
359180 2
< 0.1%
324782 1
< 0.1%
309710 2
< 0.1%
279618 1
< 0.1%
274873 1
< 0.1%
274850 2
< 0.1%
271245 2
< 0.1%
268676 2
< 0.1%
268522 2
< 0.1%
266303 1
< 0.1%

dt
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct3959
Distinct (%)91.3%
Missing1433
Missing (%)24.8%
Infinite0
Infinite (%)0.0%
Mean9866.0364
Minimum0
Maximum426314
Zeros237
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:07.031348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11033.7673
median3395.356
Q38691.365
95-th percentile32392
Maximum426314
Range426314
Interquartile range (IQR)7657.5977

Descriptive statistics

Standard deviation26541.057
Coefficient of variation (CV)2.6901438
Kurtosis82.503456
Mean9866.0364
Median Absolute Deviation (MAD)2840.5
Skewness8.0090617
Sum42759402
Variance7.044277 × 108
MonotonicityNot monotonic
2023-05-08T00:19:07.125395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237
 
4.1%
250 9
 
0.2%
500 5
 
0.1%
150 5
 
0.1%
799 3
 
0.1%
827 3
 
0.1%
35 3
 
0.1%
1199 3
 
0.1%
12825 3
 
0.1%
6838 3
 
0.1%
Other values (3949) 4060
70.4%
(Missing) 1433
 
24.8%
ValueCountFrequency (%)
0 237
4.1%
0.009 1
 
< 0.1%
0.033 1
 
< 0.1%
0.056 1
 
< 0.1%
0.2 1
 
< 0.1%
0.274 1
 
< 0.1%
0.338 1
 
< 0.1%
0.349 1
 
< 0.1%
0.509 1
 
< 0.1%
0.525 1
 
< 0.1%
ValueCountFrequency (%)
426314 1
< 0.1%
398523 1
< 0.1%
381183 1
< 0.1%
362000 1
< 0.1%
344435 1
< 0.1%
333248 1
< 0.1%
323505 1
< 0.1%
273058 1
< 0.1%
272565 1
< 0.1%
259374 1
< 0.1%

emp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2853
Distinct (%)50.2%
Missing80
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean48.41872
Minimum0.052
Maximum2300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:07.222338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.052
5-th percentile0.76
Q16.3
median16
Q348.2
95-th percentile223.715
Maximum2300
Range2299.948
Interquartile range (IQR)41.9

Descriptive statistics

Standard deviation120.63033
Coefficient of variation (CV)2.4913986
Kurtosis199.596
Mean48.41872
Median Absolute Deviation (MAD)13.1
Skewness11.86538
Sum275357.26
Variance14551.676
MonotonicityNot monotonic
2023-05-08T00:19:07.317746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 28
 
0.5%
10 25
 
0.4%
14 24
 
0.4%
30 23
 
0.4%
50 21
 
0.4%
13 20
 
0.3%
34 19
 
0.3%
25 19
 
0.3%
24 19
 
0.3%
22 19
 
0.3%
Other values (2843) 5470
94.9%
(Missing) 80
 
1.4%
ValueCountFrequency (%)
0.052 1
 
< 0.1%
0.068 1
 
< 0.1%
0.079 2
< 0.1%
0.083 2
< 0.1%
0.097 2
< 0.1%
0.114 1
 
< 0.1%
0.116 2
< 0.1%
0.125 2
< 0.1%
0.132 2
< 0.1%
0.14 3
0.1%
ValueCountFrequency (%)
2300 3
0.1%
2200 6
0.1%
2100 2
 
< 0.1%
798 1
 
< 0.1%
647.5 1
 
< 0.1%
566 1
 
< 0.1%
539 1
 
< 0.1%
537 1
 
< 0.1%
523 1
 
< 0.1%
505 1
 
< 0.1%

lct
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3906
Distinct (%)94.0%
Missing1611
Missing (%)27.9%
Infinite0
Infinite (%)0.0%
Mean6488.9805
Minimum13.203
Maximum116866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:07.413395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13.203
5-th percentile238.433
Q1950.7265
median2511.05
Q36386.475
95-th percentile27588
Maximum116866
Range116852.8
Interquartile range (IQR)5435.7485

Descriptive statistics

Standard deviation11456.573
Coefficient of variation (CV)1.7655428
Kurtosis20.262074
Mean6488.9805
Median Absolute Deviation (MAD)1891.315
Skewness3.9873915
Sum26968203
Variance1.3125306 × 108
MonotonicityNot monotonic
2023-05-08T00:19:07.509677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5374 4
 
0.1%
2467 3
 
0.1%
1501 3
 
0.1%
6570 3
 
0.1%
2265 3
 
0.1%
11971 3
 
0.1%
11593 3
 
0.1%
12708 3
 
0.1%
3485 3
 
0.1%
15014 3
 
0.1%
Other values (3896) 4125
71.5%
(Missing) 1611
 
27.9%
ValueCountFrequency (%)
13.203 1
< 0.1%
14.741 1
< 0.1%
15.129 1
< 0.1%
17.024 1
< 0.1%
18.188 1
< 0.1%
19.219 1
< 0.1%
19.5 1
< 0.1%
20.01 1
< 0.1%
23.46 1
< 0.1%
26.07 1
< 0.1%
ValueCountFrequency (%)
116866 1
< 0.1%
105718 1
< 0.1%
100814 1
< 0.1%
98132 1
< 0.1%
97312 1
< 0.1%
95569 1
< 0.1%
94600 1
< 0.1%
90281 1
< 0.1%
87812 1
< 0.1%
85181 1
< 0.1%

lt
Real number (ℝ)

Distinct4808
Distinct (%)84.0%
Missing42
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean69601.654
Minimum15.985
Maximum2426049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:07.607065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15.985
5-th percentile643.026
Q13723.338
median11054.5
Q335312
95-th percentile264313
Maximum2426049
Range2426033
Interquartile range (IQR)31588.662

Descriptive statistics

Standard deviation238771.5
Coefficient of variation (CV)3.4305435
Kurtosis47.825838
Mean69601.654
Median Absolute Deviation (MAD)9071.232
Skewness6.567469
Sum3.9846947 × 108
Variance5.7011831 × 1010
MonotonicityNot monotonic
2023-05-08T00:19:07.709710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10063 3
 
0.1%
28302 3
 
0.1%
15761 3
 
0.1%
9619.033 2
 
< 0.1%
4653.373 2
 
< 0.1%
3509.5 2
 
< 0.1%
3780.3 2
 
< 0.1%
3909.9 2
 
< 0.1%
4583 2
 
< 0.1%
4964.3 2
 
< 0.1%
Other values (4798) 5702
98.9%
(Missing) 42
 
0.7%
ValueCountFrequency (%)
15.985 1
< 0.1%
16.171 1
< 0.1%
17.024 1
< 0.1%
21.023 1
< 0.1%
21.944 1
< 0.1%
28.868 1
< 0.1%
29 1
< 0.1%
30.99 1
< 0.1%
31.548 1
< 0.1%
34.708 1
< 0.1%
ValueCountFrequency (%)
2426049 2
< 0.1%
2366017 2
< 0.1%
2341061 2
< 0.1%
2277907 2
< 0.1%
2236782 2
< 0.1%
2204511 2
< 0.1%
2169269 2
< 0.1%
2155072 2
< 0.1%
2104125 2
< 0.1%
2089182 2
< 0.1%

ni
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4477
Distinct (%)92.2%
Missing910
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean1920.1609
Minimum-23119
Maximum81417
Zeros0
Zeros (%)0.0%
Negative342
Negative (%)5.9%
Memory size45.2 KiB
2023-05-08T00:19:07.811736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-23119
5-th percentile-97.7168
Q1286.198
median704.422
Q31889
95-th percentile8169.8
Maximum81417
Range104536
Interquartile range (IQR)1602.802

Descriptive statistics

Standard deviation4385.3296
Coefficient of variation (CV)2.2838344
Kurtosis59.008623
Mean1920.1609
Median Absolute Deviation (MAD)544.295
Skewness5.8889373
Sum9326221.7
Variance19231116
MonotonicityNot monotonic
2023-05-08T00:19:07.912669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
753 4
 
0.1%
263 4
 
0.1%
477 4
 
0.1%
591 4
 
0.1%
1979 4
 
0.1%
858 4
 
0.1%
828 3
 
0.1%
1134 3
 
0.1%
242 3
 
0.1%
817 3
 
0.1%
Other values (4467) 4821
83.6%
(Missing) 910
 
15.8%
ValueCountFrequency (%)
-23119 1
< 0.1%
-22355 1
< 0.1%
-14454 1
< 0.1%
-12650 1
< 0.1%
-12236 1
< 0.1%
-10192 1
< 0.1%
-10137 1
< 0.1%
-7829 1
< 0.1%
-7642 1
< 0.1%
-6917.9 1
< 0.1%
ValueCountFrequency (%)
81417 1
< 0.1%
59531 1
< 0.1%
55256 1
< 0.1%
53394 1
< 0.1%
48351 1
< 0.1%
45687 1
< 0.1%
44940 1
< 0.1%
44880 1
< 0.1%
41733 1
< 0.1%
41060 1
< 0.1%

ppegt
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct4349
Distinct (%)86.8%
Missing756
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean14807.02
Minimum0
Maximum493335
Zeros223
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:08.006829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.9465
Q1905.5015
median3087.017
Q313230.754
95-th percentile57440.5
Maximum493335
Range493335
Interquartile range (IQR)12325.253

Descriptive statistics

Standard deviation36633.894
Coefficient of variation (CV)2.4740895
Kurtosis58.257556
Mean14807.02
Median Absolute Deviation (MAD)2788.591
Skewness6.619015
Sum74197979
Variance1.3420422 × 109
MonotonicityNot monotonic
2023-05-08T00:19:08.108204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 223
 
3.9%
2500 6
 
0.1%
2000 6
 
0.1%
1733 5
 
0.1%
3081 4
 
0.1%
119 4
 
0.1%
866 4
 
0.1%
1054 4
 
0.1%
2400 4
 
0.1%
919 4
 
0.1%
Other values (4339) 4747
82.3%
(Missing) 756
 
13.1%
ValueCountFrequency (%)
0 223
3.9%
0.827 1
 
< 0.1%
0.849 1
 
< 0.1%
0.909 1
 
< 0.1%
1.259 1
 
< 0.1%
2.313 1
 
< 0.1%
2.348 1
 
< 0.1%
2.6 1
 
< 0.1%
3.585 1
 
< 0.1%
5.251 1
 
< 0.1%
ValueCountFrequency (%)
493335 1
< 0.1%
477190 1
< 0.1%
477185 1
< 0.1%
453915 1
< 0.1%
447337 1
< 0.1%
446789 1
< 0.1%
434517 1
< 0.1%
409314 1
< 0.1%
393995 1
< 0.1%
373938 1
< 0.1%

pstkl
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct390
Distinct (%)8.0%
Missing917
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean317.7737
Minimum0
Maximum49148
Zeros4147
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:08.202498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1041.85
Maximum49148
Range49148
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2022.1028
Coefficient of variation (CV)6.3633424
Kurtosis154.19309
Mean317.7737
Median Absolute Deviation (MAD)0
Skewness11.024392
Sum1541202.4
Variance4088899.9
MonotonicityNot monotonic
2023-05-08T00:19:08.300260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4147
71.9%
200 15
 
0.3%
325 11
 
0.2%
0.3 10
 
0.2%
30.4 10
 
0.2%
39.847 10
 
0.2%
142 10
 
0.2%
575 9
 
0.2%
2100 8
 
0.1%
258 8
 
0.1%
Other values (380) 612
 
10.6%
(Missing) 917
 
15.9%
ValueCountFrequency (%)
0 4147
71.9%
0.015 3
 
0.1%
0.057 4
 
0.1%
0.058 2
 
< 0.1%
0.061 1
 
< 0.1%
0.069 1
 
< 0.1%
0.07 1
 
< 0.1%
0.236 1
 
< 0.1%
0.3 10
 
0.2%
0.551 1
 
< 0.1%
ValueCountFrequency (%)
49148 1
 
< 0.1%
26993 1
 
< 0.1%
26068 4
0.1%
25969.47 1
 
< 0.1%
25659.47 1
 
< 0.1%
25220 1
 
< 0.1%
23819.47 1
 
< 0.1%
23401 1
 
< 0.1%
23359.47 1
 
< 0.1%
22326 1
 
< 0.1%

seq
Real number (ℝ)

Distinct4787
Distinct (%)83.4%
Missing28
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean15613.757
Minimum-13244
Maximum424791
Zeros0
Zeros (%)0.0%
Negative178
Negative (%)3.1%
Memory size45.2 KiB
2023-05-08T00:19:08.401410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-13244
5-th percentile286.2702
Q12194.914
median5783.113
Q313579
95-th percentile61595.9
Maximum424791
Range438035
Interquartile range (IQR)11384.086

Descriptive statistics

Standard deviation34426.536
Coefficient of variation (CV)2.2048848
Kurtosis34.030519
Mean15613.757
Median Absolute Deviation (MAD)4267.813
Skewness5.2714837
Sum89607353
Variance1.1851864 × 109
MonotonicityNot monotonic
2023-05-08T00:19:08.494648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10381 4
 
0.1%
5793 4
 
0.1%
20673 4
 
0.1%
2162 3
 
0.1%
15023 3
 
0.1%
7336 3
 
0.1%
6070 3
 
0.1%
5055 3
 
0.1%
11390 3
 
0.1%
7217 3
 
0.1%
Other values (4777) 5706
98.9%
(Missing) 28
 
0.5%
ValueCountFrequency (%)
-13244 1
< 0.1%
-12688 1
< 0.1%
-12629 1
< 0.1%
-12459 1
< 0.1%
-12086 1
< 0.1%
-11785 1
< 0.1%
-11577 1
< 0.1%
-9660 1
< 0.1%
-8617 1
< 0.1%
-8446 1
< 0.1%
ValueCountFrequency (%)
424791 2
< 0.1%
348703 2
< 0.1%
348296 2
< 0.1%
283001 2
< 0.1%
267146 2
< 0.1%
266840 2
< 0.1%
265325 2
< 0.1%
264810 2
< 0.1%
261330 2
< 0.1%
256515 2
< 0.1%

xad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1524
Distinct (%)70.5%
Missing3605
Missing (%)62.5%
Infinite0
Infinite (%)0.0%
Mean571.4789
Minimum0
Maximum11000
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:08.590753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.205
Q137.2
median153
Q3522.61975
95-th percentile2799.9
Maximum11000
Range11000
Interquartile range (IQR)485.41975

Descriptive statistics

Standard deviation1115.1669
Coefficient of variation (CV)1.9513701
Kurtosis20.370478
Mean571.4789
Median Absolute Deviation (MAD)140.2
Skewness3.9220123
Sum1235537.4
Variance1243597.1
MonotonicityNot monotonic
2023-05-08T00:19:08.685214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2600 10
 
0.2%
2500 10
 
0.2%
1200 10
 
0.2%
1600 9
 
0.2%
30 8
 
0.1%
2400 8
 
0.1%
98 8
 
0.1%
11 7
 
0.1%
1000 7
 
0.1%
1100 7
 
0.1%
Other values (1514) 2078
36.0%
(Missing) 3605
62.5%
ValueCountFrequency (%)
0 7
0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.3 2
 
< 0.1%
0.4 2
 
< 0.1%
0.5 2
 
< 0.1%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
1 1
 
< 0.1%
1.1 3
0.1%
ValueCountFrequency (%)
11000 1
< 0.1%
9729 1
< 0.1%
9345 1
< 0.1%
9315 1
< 0.1%
9236 1
< 0.1%
8576 1
< 0.1%
8290 1
< 0.1%
8200 1
< 0.1%
7617 1
< 0.1%
7243 1
< 0.1%

xrd
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1919
Distinct (%)67.8%
Missing2938
Missing (%)50.9%
Infinite0
Infinite (%)0.0%
Mean888.34346
Minimum0
Maximum35931
Zeros651
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:08.785405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.476
median148.258
Q3651
95-th percentile5151
Maximum35931
Range35931
Interquartile range (IQR)634.524

Descriptive statistics

Standard deviation2250.3446
Coefficient of variation (CV)2.5331921
Kurtosis49.796168
Mean888.34346
Median Absolute Deviation (MAD)148.258
Skewness5.7194273
Sum2513123.7
Variance5064050.9
MonotonicityNot monotonic
2023-05-08T00:19:08.879168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 651
 
11.3%
1200 5
 
0.1%
24 5
 
0.1%
376 5
 
0.1%
96 5
 
0.1%
56 5
 
0.1%
647 4
 
0.1%
1300 4
 
0.1%
93 4
 
0.1%
33 4
 
0.1%
Other values (1909) 2137
37.1%
(Missing) 2938
50.9%
ValueCountFrequency (%)
0 651
11.3%
0.153 1
 
< 0.1%
1.109 1
 
< 0.1%
1.582 1
 
< 0.1%
1.632 1
 
< 0.1%
2.146 1
 
< 0.1%
3.198 1
 
< 0.1%
3.3 1
 
< 0.1%
3.955 1
 
< 0.1%
4.325 1
 
< 0.1%
ValueCountFrequency (%)
35931 1
< 0.1%
28837 1
< 0.1%
26018 1
< 0.1%
22620 1
< 0.1%
21419 1
< 0.1%
16876 1
< 0.1%
16625 1
< 0.1%
16217 1
< 0.1%
16085 1
< 0.1%
14726 1
< 0.1%

cik
Real number (ℝ)

Distinct498
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean763585.01
Minimum1800
Maximum1932393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:08.982060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1800
5-th percentile16918
Q193483
median882095
Q31121788
95-th percentile1601046
Maximum1932393
Range1930593
Interquartile range (IQR)1028305

Descriptive statistics

Standard deviation534566.69
Coefficient of variation (CV)0.70007489
Kurtosis-1.1950413
Mean763585.01
Median Absolute Deviation (MAD)407395
Skewness-0.12138091
Sum4.4035948 × 109
Variance2.8576155 × 1011
MonotonicityNot monotonic
2023-05-08T00:19:09.085896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
731766 20
 
0.3%
35527 20
 
0.3%
49196 20
 
0.3%
93751 20
 
0.3%
1393311 20
 
0.3%
1099219 20
 
0.3%
766704 20
 
0.3%
896159 20
 
0.3%
49071 20
 
0.3%
320335 20
 
0.3%
Other values (488) 5567
96.5%
ValueCountFrequency (%)
1800 10
0.2%
2488 10
0.2%
2969 10
0.2%
4127 10
0.2%
4281 8
 
0.1%
4447 10
0.2%
4904 10
0.2%
4962 20
0.3%
4977 20
0.3%
5272 20
0.3%
ValueCountFrequency (%)
1932393 1
 
< 0.1%
1841666 10
0.2%
1821825 2
 
< 0.1%
1792044 10
0.2%
1783180 5
0.1%
1781335 5
0.1%
1757898 10
0.2%
1755672 6
0.1%
1754301 6
0.1%
1751788 5
0.1%

costat
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.2 KiB
A
5767 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5767
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 5767
100.0%

Length

2023-05-08T00:19:09.175767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:19:09.244153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
a 5767
100.0%

Most occurring characters

ValueCountFrequency (%)
A 5767
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5767
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 5767
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5767
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 5767
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 5767
100.0%

mkvalt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4478
Distinct (%)88.2%
Missing689
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean37144.279
Minimum62.8917
Maximum1073390.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:09.317967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum62.8917
5-th percentile3346.6943
Q18459.8793
median15922.04
Q333569.9
95-th percentile157511.87
Maximum1073390.5
Range1073327.6
Interquartile range (IQR)25110.021

Descriptive statistics

Standard deviation69968.522
Coefficient of variation (CV)1.8836958
Kurtosis55.204443
Mean37144.279
Median Absolute Deviation (MAD)9335.5166
Skewness6.0304569
Sum1.8861865 × 108
Variance4.8955941 × 109
MonotonicityNot monotonic
2023-05-08T00:19:09.448808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36812.696 3
 
0.1%
44701.4704 2
 
< 0.1%
18240.9567 2
 
< 0.1%
300680.13 2
 
< 0.1%
194498.15 2
 
< 0.1%
159533.44 2
 
< 0.1%
111613.25 2
 
< 0.1%
108653.76 2
 
< 0.1%
13570.7011 2
 
< 0.1%
13822.4011 2
 
< 0.1%
Other values (4468) 5057
87.7%
(Missing) 689
 
11.9%
ValueCountFrequency (%)
62.8917 1
< 0.1%
117.3438 1
< 0.1%
149.1244 1
< 0.1%
160.8317 1
< 0.1%
188.0294 1
< 0.1%
207.0527 1
< 0.1%
266.5571 1
< 0.1%
267.0598 1
< 0.1%
506.2756 1
< 0.1%
509.7578 1
< 0.1%
ValueCountFrequency (%)
1073390.54 1
< 0.1%
1023856.28 1
< 0.1%
995151.5669 1
< 0.1%
921138.3192 1
< 0.1%
920224.32 1
< 0.1%
790050.0981 1
< 0.1%
757028.97 1
< 0.1%
737467.27 1
< 0.1%
729439.0252 1
< 0.1%
723558.7085 1
< 0.1%

gsector
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.915554
Minimum10
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:09.530529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q125
median40
Q345
95-th percentile60
Maximum60
Range50
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.506822
Coefficient of variation (CV)0.37607167
Kurtosis-0.7420933
Mean35.915554
Median Absolute Deviation (MAD)10
Skewness0.004581157
Sum207125
Variance182.43425
MonotonicityNot monotonic
2023-05-08T00:19:09.600219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
40 1334
23.1%
20 727
12.6%
35 674
11.7%
45 622
10.8%
25 529
 
9.2%
60 509
 
8.8%
30 363
 
6.3%
55 290
 
5.0%
15 282
 
4.9%
10 226
 
3.9%
ValueCountFrequency (%)
10 226
 
3.9%
15 282
 
4.9%
20 727
12.6%
25 529
 
9.2%
30 363
 
6.3%
35 674
11.7%
40 1334
23.1%
45 622
10.8%
50 211
 
3.7%
55 290
 
5.0%
ValueCountFrequency (%)
60 509
 
8.8%
55 290
 
5.0%
50 211
 
3.7%
45 622
10.8%
40 1334
23.1%
35 674
11.7%
30 363
 
6.3%
25 529
 
9.2%
20 727
12.6%
15 282
 
4.9%

naics
Real number (ℝ)

Distinct206
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean407659.07
Minimum42
Maximum999977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.2 KiB
2023-05-08T00:19:09.689143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile2211
Q1331110
median482111
Q3524113
95-th percentile561450
Maximum999977
Range999935
Interquartile range (IQR)193003

Descriptive statistics

Standard deviation173038.13
Coefficient of variation (CV)0.42446775
Kurtosis0.95624148
Mean407659.07
Median Absolute Deviation (MAD)59401
Skewness-0.74744093
Sum2.3509699 × 109
Variance2.9942194 × 1010
MonotonicityNot monotonic
2023-05-08T00:19:09.791587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
522110 376
 
6.5%
531120 263
 
4.6%
524126 220
 
3.8%
334413 168
 
2.9%
531110 166
 
2.9%
22111 140
 
2.4%
523940 130
 
2.3%
524114 120
 
2.1%
2211 120
 
2.1%
325412 110
 
1.9%
Other values (196) 3954
68.6%
ValueCountFrequency (%)
42 10
 
0.2%
111 6
 
0.1%
315 19
 
0.3%
321 10
 
0.2%
325 10
 
0.2%
423 10
 
0.2%
621 10
 
0.2%
2111 100
1.7%
2211 120
2.1%
3113 10
 
0.2%
ValueCountFrequency (%)
999977 30
0.5%
812331 10
 
0.2%
722513 40
0.7%
722511 20
0.3%
721120 30
0.5%
721110 20
0.3%
713210 6
 
0.1%
711320 10
 
0.2%
622110 20
0.3%
621511 20
0.3%

ipodate
Categorical

HIGH CARDINALITY  MISSING 

Distinct204
Distinct (%)8.0%
Missing3232
Missing (%)56.0%
Memory size45.2 KiB
1/1/87
 
40
7/2/07
 
30
3/24/93
 
20
3/1/94
 
20
12/20/95
 
20
Other values (199)
2405 

Length

Max length8
Median length7
Mean length6.97357
Min length6

Characters and Unicode

Total characters17678
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/1/87
2nd row1/1/87
3rd row1/1/87
4th row1/1/87
5th row1/1/87

Common Values

ValueCountFrequency (%)
1/1/87 40
 
0.7%
7/2/07 30
 
0.5%
3/24/93 20
 
0.3%
3/1/94 20
 
0.3%
12/20/95 20
 
0.3%
11/16/95 20
 
0.3%
10/2/95 20
 
0.3%
9/13/95 20
 
0.3%
6/20/95 20
 
0.3%
11/15/94 20
 
0.3%
Other values (194) 2305
40.0%
(Missing) 3232
56.0%

Length

2023-05-08T00:19:09.883889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1/1/87 40
 
1.6%
7/2/07 30
 
1.2%
12/13/01 20
 
0.8%
11/16/05 20
 
0.8%
10/29/04 20
 
0.8%
3/23/04 20
 
0.8%
8/12/04 20
 
0.8%
5/25/06 20
 
0.8%
11/5/04 20
 
0.8%
2/5/04 20
 
0.8%
Other values (194) 2305
90.9%

Most occurring characters

ValueCountFrequency (%)
/ 5070
28.7%
1 2785
15.8%
2 1882
 
10.6%
9 1837
 
10.4%
0 1341
 
7.6%
3 947
 
5.4%
6 836
 
4.7%
4 785
 
4.4%
5 783
 
4.4%
8 721
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12608
71.3%
Other Punctuation 5070
28.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2785
22.1%
2 1882
14.9%
9 1837
14.6%
0 1341
10.6%
3 947
 
7.5%
6 836
 
6.6%
4 785
 
6.2%
5 783
 
6.2%
8 721
 
5.7%
7 691
 
5.5%
Other Punctuation
ValueCountFrequency (%)
/ 5070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 5070
28.7%
1 2785
15.8%
2 1882
 
10.6%
9 1837
 
10.4%
0 1341
 
7.6%
3 947
 
5.4%
6 836
 
4.7%
4 785
 
4.4%
5 783
 
4.4%
8 721
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 5070
28.7%
1 2785
15.8%
2 1882
 
10.6%
9 1837
 
10.4%
0 1341
 
7.6%
3 947
 
5.4%
6 836
 
4.7%
4 785
 
4.4%
5 783
 
4.4%
8 721
 
4.1%

size_category
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.1%
Missing689
Missing (%)11.9%
Memory size45.2 KiB
Big
3080 
Medium
1487 
Huge
421 
Tiny
 
90

Length

Max length6
Median length3
Mean length3.9791256
Min length3

Characters and Unicode

Total characters20206
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowTiny
3rd rowTiny
4th rowMedium
5th rowBig

Common Values

ValueCountFrequency (%)
Big 3080
53.4%
Medium 1487
25.8%
Huge 421
 
7.3%
Tiny 90
 
1.6%
(Missing) 689
 
11.9%

Length

2023-05-08T00:19:09.967918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:19:10.056896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
big 3080
60.7%
medium 1487
29.3%
huge 421
 
8.3%
tiny 90
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i 4657
23.0%
g 3501
17.3%
B 3080
15.2%
e 1908
9.4%
u 1908
9.4%
M 1487
 
7.4%
d 1487
 
7.4%
m 1487
 
7.4%
H 421
 
2.1%
T 90
 
0.4%
Other values (2) 180
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15128
74.9%
Uppercase Letter 5078
 
25.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4657
30.8%
g 3501
23.1%
e 1908
12.6%
u 1908
12.6%
d 1487
 
9.8%
m 1487
 
9.8%
n 90
 
0.6%
y 90
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
B 3080
60.7%
M 1487
29.3%
H 421
 
8.3%
T 90
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 20206
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4657
23.0%
g 3501
17.3%
B 3080
15.2%
e 1908
9.4%
u 1908
9.4%
M 1487
 
7.4%
d 1487
 
7.4%
m 1487
 
7.4%
H 421
 
2.1%
T 90
 
0.4%
Other values (2) 180
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 4657
23.0%
g 3501
17.3%
B 3080
15.2%
e 1908
9.4%
u 1908
9.4%
M 1487
 
7.4%
d 1487
 
7.4%
m 1487
 
7.4%
H 421
 
2.1%
T 90
 
0.4%
Other values (2) 180
 
0.9%

Interactions

2023-05-08T00:18:59.802866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:03.044047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-05-08T00:18:57.473497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:59.630884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:19:01.910011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:05.156519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:07.327874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:09.522966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:11.651507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:14.001752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:16.330074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:18.658291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:20.931999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:25.392074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:27.596956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:29.866044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:32.363429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:34.453462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:36.672250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:38.859155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:41.489529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:43.688307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:45.946160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:48.092208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:50.236771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:53.003917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:55.353150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:57.558869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:18:59.715264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-08T00:19:10.158059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0gvkeyfyearfyractatbkvlpscapxceqlcshodlttdtemplctltnippegtpstklseqxadxrdcikmkvaltgsectornaicsindfmtsize_category
Unnamed: 01.0001.0000.0160.088-0.258-0.293-0.054-0.297-0.281-0.335-0.283-0.252-0.332-0.295-0.289-0.283-0.380-0.064-0.269-0.311-0.2260.656-0.2640.1270.2470.1180.164
gvkey1.0001.0000.0140.088-0.258-0.293-0.055-0.298-0.281-0.335-0.284-0.252-0.333-0.296-0.289-0.284-0.380-0.064-0.269-0.312-0.2260.656-0.2650.1270.2470.0820.142
fyear0.0160.0141.0000.0580.1280.1480.1290.0980.1180.0520.2040.2220.0660.1620.1480.1420.1050.0140.1230.1030.0740.0250.2930.0020.0010.0000.174
fyr0.0880.0880.0581.000-0.0530.1660.174-0.0230.080-0.0280.1890.133-0.1500.0050.179-0.022-0.0040.1440.113-0.064-0.0000.0290.0020.0680.1440.1790.076
act-0.258-0.2580.128-0.0531.0000.8290.1960.6130.7120.7390.5930.6450.6450.9050.7930.7070.5670.0600.7170.6540.525-0.1590.761-0.002-0.0700.1040.363
at-0.293-0.2930.1480.1660.8291.0000.3960.5670.8690.7480.8140.8400.5150.9030.9780.6950.6480.3010.8810.6210.401-0.1810.7140.0860.0570.1990.238
bkvlps-0.054-0.0550.1290.1740.1960.3961.0000.1030.561-0.1070.2190.1860.0400.1960.3130.2330.2270.1660.5610.0980.003-0.0060.1750.035-0.0131.0001.000
capx-0.297-0.2980.098-0.0230.6130.5670.1031.0000.5070.5520.5770.5960.5620.7160.5510.4790.954-0.0480.5020.6170.438-0.1070.580-0.215-0.3771.0000.257
ceql-0.281-0.2810.1180.0800.7120.8690.5610.5071.0000.7060.6560.6260.4410.7080.7630.6630.6590.2610.9990.5430.424-0.1520.7070.080-0.0761.0000.374
csho-0.335-0.3350.052-0.0280.7390.748-0.1070.5520.7061.0000.6590.6510.5470.7420.7010.6370.6610.1740.7050.6170.493-0.2090.7320.050-0.0981.0000.327
dltt-0.283-0.2840.2040.1890.5930.8140.2190.5770.6560.6591.0000.9970.4760.7120.8360.5580.6840.2610.6760.5650.205-0.1490.6660.076-0.0080.1260.257
dt-0.252-0.2520.2220.1330.6450.8400.1860.5960.6260.6510.9971.0000.5140.7380.8800.5540.7680.1700.6260.5730.215-0.1370.676-0.036-0.0051.0000.200
emp-0.332-0.3330.066-0.1500.6450.5150.0400.5620.4410.5470.4760.5141.0000.6690.5290.5840.615-0.0350.4350.6430.286-0.2230.580-0.316-0.0740.0090.221
lct-0.295-0.2960.1620.0050.9050.9030.1960.7160.7080.7420.7120.7380.6691.0000.9110.6930.6720.1060.7120.7100.431-0.1580.7550.025-0.0390.1570.347
lt-0.289-0.2890.1480.1790.7930.9780.3130.5510.7630.7010.8360.8800.5290.9111.0000.6540.6400.2910.7830.6060.351-0.1870.6640.0650.0750.2080.225
ni-0.283-0.2840.142-0.0220.7070.6950.2330.4790.6630.6370.5580.5540.5840.6930.6541.0000.5480.1060.6610.6180.373-0.1970.786-0.044-0.0371.0000.397
ppegt-0.380-0.3800.105-0.0040.5670.6480.2270.9540.6590.6610.6840.7680.6150.6720.6400.5481.0000.2020.5520.6690.307-0.1790.598-0.226-0.3870.0740.241
pstkl-0.064-0.0640.0140.1440.0600.3010.166-0.0480.2610.1740.2610.170-0.0350.1060.2910.1060.2021.0000.2800.115-0.126-0.0550.1150.3090.1151.0000.144
seq-0.269-0.2690.1230.1130.7170.8810.5610.5020.9990.7050.6760.6260.4350.7120.7830.6610.5520.2801.0000.5400.418-0.1540.7090.1010.0000.1240.374
xad-0.311-0.3120.103-0.0640.6540.6210.0980.6170.5430.6170.5650.5730.6430.7100.6060.6180.6690.1150.5401.0000.324-0.2290.6820.009-0.1331.0000.343
xrd-0.226-0.2260.074-0.0000.5250.4010.0030.4380.4240.4930.2050.2150.2860.4310.3510.3730.307-0.1260.4180.3241.000-0.1410.4940.048-0.3431.0000.324
cik0.6560.6560.0250.029-0.159-0.181-0.006-0.107-0.152-0.209-0.149-0.137-0.223-0.158-0.187-0.197-0.179-0.055-0.154-0.229-0.1411.000-0.1670.0730.1070.0690.151
mkvalt-0.264-0.2650.2930.0020.7610.7140.1750.5800.7070.7320.6660.6760.5800.7550.6640.7860.5980.1150.7090.6820.494-0.1671.000-0.034-0.0520.0310.548
gsector0.1270.1270.0020.068-0.0020.0860.035-0.2150.0800.0500.076-0.036-0.3160.0250.065-0.044-0.2260.3090.1010.0090.0480.073-0.0341.0000.3590.5610.153
naics0.2470.2470.0010.144-0.0700.057-0.013-0.377-0.076-0.098-0.008-0.005-0.074-0.0390.075-0.037-0.3870.1150.000-0.133-0.3430.107-0.0520.3591.0000.4680.135
indfmt0.1180.0820.0000.1790.1040.1991.0001.0001.0001.0000.1261.0000.0090.1570.2081.0000.0741.0000.1241.0001.0000.0690.0310.5610.4681.0000.032
size_category0.1640.1420.1740.0760.3630.2381.0000.2570.3740.3270.2570.2000.2210.3470.2250.3970.2410.1440.3740.3430.3240.1510.5480.1530.1350.0321.000

Missing values

2023-05-08T00:19:02.082670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-08T00:19:02.572396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-08T00:19:02.882519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0gvkeydatadatefyearindfmtconsolpopsrcdatafmtticcusipconmcurcdfyractatbkvlpscapxceqlcshodlttdtemplctltnippegtpstklseqxadxrdcikcostatmkvaltgsectornaicsipodatesize_category
00104512/31/102010.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.06838.025088.0-11.83091962.0-3945.0333.4509253.011136.078.258780.029033.0-471.026717.00.0-3945.0165.0NaN6201A2597.575520481111NaNMedium
11104512/31/112011.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.06757.023848.0-21.20991610.0-7111.0335.2686702.08220.080.108630.030959.0-1979.024854.00.0-7111.0186.0NaN6201A117.343820481111NaNTiny
22104512/31/122012.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.07072.023510.0-23.82101888.0-7987.0335.2927116.08535.077.759304.031497.0-1876.024233.00.0-7987.0153.0NaN6201A266.557120481111NaNTiny
33104512/31/132013.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.014323.042278.0-10.46083114.0-2731.0261.06915353.016799.0110.4013806.045009.0-1834.030392.00.0-2731.0166.0NaN6201A6591.992320481111NaNMedium
44104512/31/142014.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.012112.043771.02.89765311.02021.0697.47516196.017904.0113.3013435.041750.02882.035343.00.02021.0100.0NaN6201A37405.584320481111NaNBig
55104512/31/152015.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.09985.048415.09.02156151.05635.0624.62218330.020561.0118.5013605.042780.07610.040654.00.05635.0110.0NaN6201A26452.741720481111NaNBig
66104512/31/162016.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.010324.051274.07.46125731.03785.0507.29422489.024344.0122.3013872.047489.02676.045353.00.03785.0116.0NaN6201A23685.556920481111NaNBig
77104512/31/172017.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.09146.051396.08.25645971.03926.0475.50822511.025065.0126.6014964.047470.01919.049802.00.03926.0135.0NaN6201A24740.681220481111NaNBig
88104512/31/182018.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.08637.060580.0-0.36693745.0-169.0460.61129081.034029.0128.9018096.060749.01412.060692.00.0-169.0128.0NaN6201A14790.219220481111NaNBig
99104512/31/192019.0INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD12.08206.059995.0-0.27564268.0-118.0428.20328875.033444.0133.7018311.060113.01686.062391.00.0-118.0129.0NaN6201A12280.862020481111NaNBig
Unnamed: 0gvkeydatadatefyearindfmtconsolpopsrcdatafmtticcusipconmcurcdfyractatbkvlpscapxceqlcshodlttdtemplctltnippegtpstklseqxadxrdcikcostatmkvaltgsectornaicsipodatesize_category
5757575729452412/31/192019.0INDLCDSTDLYBN53745100LYONDELLBASELL INDUSTRIES NVUSD12.09510.030435.024.12162694.08044.0333.47712830.013106.019.15198.022256.03390.022728.00.08044.0NaN111.01489393A31506.907015325220NaNBig
5758575831605612/31/112011.0INDLCDSTDALLEG0176J109ALLEGION PLCUSD12.0961.72036.2NaN25.51413.8NaN3.54.9NaN374.5600.4218.1558.00.01413.8NaN38.91579241ANaN20332510NaNNaN
5759575931605612/31/122012.0INDLCDSTDALLEG0176J109ALLEGION PLCUSD12.0909.91983.8NaN19.61343.2NaN2.83.77.6382.7617.6219.6586.80.01343.2NaN38.21579241ANaN20332510NaNNaN
5760576031605612/31/132013.0INDLCDSTDALLEG0176J109ALLEGION PLCUSD12.0923.21979.9-0.903920.2-86.896.0291272.01302.08.0490.52035.631.0569.20.0-86.8NaN39.61579241A4243.521520332510NaNMedium
5761576131605612/31/142014.0INDLCDSTDALLEG0176J109ALLEGION PLCUSD12.0973.82015.9-0.050151.5-4.895.8311215.01215.08.5531.31997.4175.2586.80.0-4.8NaN43.31579241A5314.787320332510NaNMedium
5762576231605612/31/152015.0INDLCDSTDALLEG0176J109ALLEGION PLCUSD12.0735.12285.30.266735.225.695.9911479.81479.89.4447.12255.6153.9616.50.025.6NaN45.21579241A6327.726720332510NaNMedium
5763576331605612/31/162016.0INDLCDSTDALLEG0176J109ALLEGION PLCUSD12.0829.32247.41.189242.5113.395.2741415.61463.89.4429.62131.0229.1640.40.0113.3NaN47.31579241A6097.536020332510NaNMedium
5764576431605612/31/172017.0INDLCDSTDALLEG0176J109ALLEGION PLCUSD12.01032.72542.04.224649.3401.695.0621442.31477.310.0460.82136.5273.3707.90.0401.6NaN48.31579241A7563.132720332510NaNMedium
5765576531605612/31/182018.0INDLCDSTDALLEG0176J109ALLEGION PLCUSD12.0931.62810.26.878949.1651.094.6371409.51444.811.0520.82156.2434.9748.80.0651.0NaN54.41579241A7543.515320332510NaNMedium
5766576631605612/31/192019.0INDLCDSTDALLEG0176J109ALLEGION PLCUSD12.01001.82967.28.168365.6757.492.7241483.21509.111.0507.02206.8401.8867.40.0757.4NaN54.71579241A11547.847020332510NaNBig